Parametric Time-to-Event Analysis with Variable Incubation Phases

Fit parametric models for time-to-event data that show an initial 'incubation period', i.e., a variable delay phase where the hazard is zero. The delayed Weibull distribution serves as foundational data model. The specific method of 'MPSE' (maximum product of spacings estimation) and MLE-based methods are used for parameter estimation. Bootstrap confidence intervals for parameters and significance tests in a two group setting are provided.

Get Data from the 'KOF Datenservice' API

Read Swiss time series data from the 'KOF Data' API, < https://datenservice.kof.ethz.ch>. The API provides macro economic time series data mostly about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token.

Response Quality Indicators for Survey Research

Calculate common survey data quality indicators for multi-item scales and matrix questions. Currently supports the calculation of response style indicators and response distribution indicators. For an overview on response quality indicators see Bhaktha N, Henning S, Clemens L (2024). 'Characterizing response quality in surveys with multi-item scales: A unified framework' < https://osf.io/9gs67/>.

Agreement of Nominal Scale Raters (with a Gold Standard)

Estimate agreement of a group of raters with a gold standard rating on a nominal scale. For a single gold standard rater the average pairwise agreement of raters with this gold standard is provided. For a group of (gold standard) raters the approach of S. Vanbelle, A. Albert (2009) is implemented. Bias and standard error are estimated via delete-1 jackknife.

PARAFAC Analysis of EEMs from DOM

This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) . The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.

Easy Computation of Functional Diversity Indices

Computes six functional diversity indices. These are namely, Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic), Functional Richness intersections (FRic_intersect), Functional Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008 ). Provides efficient, modular, and parallel functions to compute functional diversity indices (preprint: ).

Dynamic Path Analysis of Survival Data via Aalen's Additive Hazards Model

Dynamic path analysis with estimation of the corresponding direct, indirect, and total effects, based on Fosen et al., (2006) . The main outcome of interest is a counting process from survival analysis (or recurrent events) data. At each time of event, ordinary linear regression is used to estimate the relation between the covariates, while Aalen's additive hazard model is used for the regression of the counting process on the covariates.

Taxonomic Information from Around the Web

Interacts with a suite of web application programming interfaces (API) for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more. Some of the services supported include 'NCBI E-utilities' (< https://www.ncbi.nlm.nih.gov/books/NBK25501/>), 'Encyclopedia of Life' (< https://eol.org/docs/what-is-eol/data-services>), 'Global Biodiversity Information Facility' (< https://techdocs.gbif.org/en/openapi/>), and many more. Links to the API documentation for other supported services are available in the documentation for their respective functions in this package.

Fitting Distributions to Given Data or Known Quantiles

Collection of functions for fitting distributions to given data or by known quantiles. Two main functions fit.perc() and fit.cont() provide users a GUI that allows to choose a most appropriate distribution without any knowledge of the R syntax. Note, this package is a part of the 'rrisk' project.

Simulation Classes Based on Package 'distr'

S4-classes for setting up a coherent framework for simulation within the distr family of packages.